To thoroughly validate the performance of our module, we first perform ablation studies, and then compare ConAM with four different kinds of state-of-the-art CNNs, including classical models, models generated by neural architecture search (NAS) [1]}, [2]}, [3]}, which is divided into semi-automatic NAS and full-automatic NAS according to their search method, and the models based on the popular existing attention mechanisms. Each experiment is performed three times due to the high computational overhead, and the average results are presented. Besides, to facilitate the exploration of the influence of different patch sizes, we modify the first layer with filter size of 7 and stride of 2 into the layer with filter size of 3 and stride of 1. Meanwhile, the following MaxPooling is deleted. Hence the input spatial size of each block in ResNet [4]} is transformed from [8, 8, 4, 2] to [32, 32, 16, 8] and this gives greater flexibility in designing different patch sizes to study the impact of patch size. Every model in all experiments follows this modification.
